A validated in-house assay for HIV drug resistance mutation surveillance from dried blood spot specimens
Bibliographic record
Abstract
Despite increasing scale-up of antiretroviral therapy (ART) coverage, challenges related to adherence and HIV drug resistance (HIVDR) remain. The high cost of HIVDR surveillance is a persistent challenge with implementation in resource-constrained settings. Dried blood spot (DBS) specimens have been demonstrated to be a feasible alternative to plasma or serum for HIVDR genotyping and are more suitable for lower resource settings. There is a need for affordable HIVDR genotyping assays which can amplify HIV-1 sequences from DBS specimens, particularly those with low viral loads, at a low cost. Here, we present an in-house assay capable of reliably amplifying HIV-1 protease and partial reverse transcriptase genes from DBS specimens, which covers the complete World Health Organization 2009 list of drug resistance mutations under surveillance. DBS specimens were prepared using whole blood spiked with HIV-1 at concentrations of 10,000, 5000, 1000, and 500 copies/mL (n=30 for each concentration). Specimens were tested in triplicate. A two-step approach was used consisting of cDNA synthesis followed by nested PCR. The limit of detection of the assay was calculated to be approximately 5000 (95% CI: 3200-10,700) copies/mL for the protease gene and 3600 (95% CI: 2200-10,000) copies/mL for reverse transcriptase. The assay was observed to be most sensitive with higher viral load specimens (97.8% [95% CI: 92.2-99.7]) for both protease and reverse transcriptase at 10,000 copies/mL with performance decreasing with the use of specimens with lower viral loads (46.7% [36.1-57.5] and 60.0% [49.1-70.2] at 500 copies/mL for protease and reverse transcriptase, respectively). Ultimately, this assay presents a promising opportunity for use in resource-constrained settings. Future work should involve validation under field conditions including sub-optimal storage conditions and preparation of DBS with fingerprick blood in order to accurately reflect real-world collection scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".